class_docstr: Wrapper for loading LightGlue-ONNX models and running inference via ONNXRuntime.
  
  LightGlue :cite:`LightGlue2023` performs fast descriptor-based deep keypoint matching.
  This module requires `onnxruntime` to be installed.
  
  If you have trained your own LightGlue model, see https://github.com/fabio-sim/LightGlue-ONNX
  for how to export the model to ONNX and optimize it.
  
  Args:
      weights: Pretrained weights, or a path to your own exported ONNX model. Available pretrained weights
        are ``'disk'``, ``'superpoint'``, ``'disk_fp16'``, and ``'superpoint_fp16'``. `Note that FP16 requires CUDA.`
        Defaults to ``'disk_fp16'`` if ``device`` is CUDA, and ``'disk'`` if CPU.
      device: Device to run inference on.
code: def __call__(
      self, data: dict[str, dict[str, Tensor]]
  ) -> dict[str, Tensor]:
      return self.forward(data)
  
file_path: kornia/kornia/feature/lightglue_onnx/lightglue.py
imports: from __future__ import annotations
  from typing import ClassVar
  import torch
  from kornia.core import Device, Tensor
  from kornia.core.check import KORNIA_CHECK, KORNIA_CHECK_SAME_DEVICES, KORNIA_CHECK_SHAPE
  from .utils import download_onnx_from_url, normalize_keypoints
label_desc: Use image node_id 2 for calling the OnnxLightGlue obj's underlying member method code with example usage: obj.__call__(data) and returns: self